






"""simple docstring"""







import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType


class        __UpperCAmelCase(  UpperCAmelCase__				):




						"""simple docstring"""




						__lowerCamelCase										=   ["image_processor", "tokenizer"]
						__lowerCamelCase										=   "LayoutLMv3ImageProcessor"
						__lowerCamelCase										=   ("LayoutLMv3Tokenizer", "LayoutLMv3TokenizerFast")




						def __init__(  self			,    snake_case__=None			,    snake_case__=None			,    **snake_case__     ):
												'''simple docstring'''
												lowercase__							: Union[str, Any]=			None
												if "feature_extractor" in kwargs:
																		warnings.warn(
																		    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
																		    " instead."			,    snake_case__			,    )
																		lowercase__							: Tuple=			kwargs.pop("feature_extractor"     )

												lowercase__							: Union[str, Any]=			image_processor if image_processor is not None else feature_extractor
												if image_processor is None:
																		raise ValueError("You need to specify an `image_processor`."     )
												if tokenizer is None:
																		raise ValueError("You need to specify a `tokenizer`."     )

												super().__init__(snake_case__			,    snake_case__     )




						def __call__(  self			,    snake_case__			,    snake_case__ = None			,    snake_case__ = None			,    snake_case__ = None			,    snake_case__ = None			,    snake_case__ = True			,    snake_case__ = False			,    snake_case__ = None			,    snake_case__ = None			,    snake_case__ = 0			,    snake_case__ = None			,    snake_case__ = None			,    snake_case__ = None			,    snake_case__ = False			,    snake_case__ = False			,    snake_case__ = False			,    snake_case__ = False			,    snake_case__ = True			,    snake_case__ = None			,    **snake_case__			,    ):
												'''simple docstring'''



												if self.image_processor.apply_ocr and (boxes is not None):
																		raise ValueError(
																		    "You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True."     )

												if self.image_processor.apply_ocr and (word_labels is not None):
																		raise ValueError(
																		    "You cannot provide word labels if you initialized the image processor with apply_ocr set to True."     )

												# first, apply the image processor
												lowercase__							: Any=			self.image_processor(images=snake_case__			,    return_tensors=snake_case__     )

												# second, apply the tokenizer
												if text is not None and self.image_processor.apply_ocr and text_pair is None:
																		if isinstance(snake_case__			,    snake_case__     ):
																								lowercase__							: List[str]=			[text]  # add batch dimension (as the image processor always adds a batch dimension)
																		lowercase__							: Union[str, Any]=			features["words"]

												lowercase__							: Optional[Any]=			self.tokenizer(
												    text=text if text is not None else features["words"]			,    text_pair=text_pair if text_pair is not None else None			,    boxes=boxes if boxes is not None else features["boxes"]			,    word_labels=snake_case__			,    add_special_tokens=snake_case__			,    padding=snake_case__			,    truncation=snake_case__			,    max_length=snake_case__			,    stride=snake_case__			,    pad_to_multiple_of=snake_case__			,    return_token_type_ids=snake_case__			,    return_attention_mask=snake_case__			,    return_overflowing_tokens=snake_case__			,    return_special_tokens_mask=snake_case__			,    return_offsets_mapping=snake_case__			,    return_length=snake_case__			,    verbose=snake_case__			,    return_tensors=snake_case__			,    **snake_case__			,    )

												# add pixel values
												lowercase__							: List[str]=			features.pop("pixel_values"     )
												if return_overflowing_tokens is True:
																		lowercase__							: Tuple=			self.get_overflowing_images(snake_case__			,    encoded_inputs["overflow_to_sample_mapping"]     )
												lowercase__							: Tuple=			images

												return encoded_inputs




						def 						UpperCAmelCase_			(  self			,    snake_case__			,    snake_case__     ):
												'''simple docstring'''
												lowercase__							: Tuple=			[]
												for sample_idx in overflow_to_sample_mapping:
																		images_with_overflow.append(images[sample_idx]     )

												if len(snake_case__     ) != len(snake_case__     ):
																		raise ValueError(
																		    "Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"
																		    F''' {len(snake_case__     )} and {len(snake_case__     )}'''     )

												return images_with_overflow




						def 						UpperCAmelCase_			(  self			,    *snake_case__			,    **snake_case__     ):
												'''simple docstring'''
												return self.tokenizer.batch_decode(*snake_case__			,    **snake_case__     )




						def 						UpperCAmelCase_			(  self			,    *snake_case__			,    **snake_case__     ):
												'''simple docstring'''
												return self.tokenizer.decode(*snake_case__			,    **snake_case__     )




						@property
						def 						UpperCAmelCase_			(  self     ):
												'''simple docstring'''
												return ["input_ids", "bbox", "attention_mask", "pixel_values"]




						@property
						def 						UpperCAmelCase_			(  self     ):
												'''simple docstring'''
												warnings.warn(
												    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."			,    snake_case__			,    )
												return self.image_processor_class




						@property
						def 						UpperCAmelCase_			(  self     ):
												'''simple docstring'''
												warnings.warn(
												    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."			,    snake_case__			,    )
												return self.image_processor







'''simple docstring'''




def __lowerCAmelCase					(				UpperCamelCase__							)      ->       bool:
    if not isinstance(UpperCamelCase__							,    UpperCamelCase__							):
        raise ValueError('''Input series is not valid, valid series - [2, 4, 6]'''							)
    if len(UpperCamelCase__							) == 0:
        raise ValueError('''Input list must be a non empty list'''							)
    if len(UpperCamelCase__							) == 1:
        return True
    __lowerCamelCase            =    series[1] - series[0]
    for index in range(len(UpperCamelCase__							) - 1							):
        if series[index + 1] - series[index] != common_diff:
            return False
    return True






def __lowerCAmelCase					(				UpperCamelCase__							)      ->       float:
    if not isinstance(UpperCamelCase__							,    UpperCamelCase__							):
        raise ValueError('''Input series is not valid, valid series - [2, 4, 6]'''							)
    if len(UpperCamelCase__							) == 0:
        raise ValueError('''Input list must be a non empty list'''							)
    __lowerCamelCase            =    0
    for val in series:
        answer += val
    return answer / len(UpperCamelCase__							)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
